Comparison shopping services like Froogle, Amazon, Yahoo Shopping, PriceWatch, NexTag, and Shopping.com provide on-line merchants with a free or relatively inexpensive way to gain access to new prospective customers. An online merchant provides one or more product descriptions and the corresponding pricing for display in a comparative listing with the same or similar products offered by other merchants. This may include just the goods price for each merchant or also shipping and handling pricing (and any necessary tax calculation), so that a true total price can be determined by a visitor to the comparison shopping service who wishes to explore and to place an order to a listed merchant. However, the very nature of comparison-shopping leads to price sensitive purchases and may tend to reduce the profit margins of online merchants that participate in such services.
It is often difficult for merchants to know the price at which their competition is selling a particular product. A number of other patent applications (e.g., U.S. Patent Publication Nos. 20040249643 and 20050071249) address this problem. However, the systems described in those publications focus on price setting and do not consider other adaptive changes such as modifying shipping cost that might affect marketplace positioning, nor do they address how to administer more than one price per item or the cost or type of the sales channels being used. In the following discussion of the invention, the term channel includes but is not limited to at least one comparison shopping service, at least one marketplace, at least one merchant shopping site or a combination of the three (for example a group comprising two comparison shopping services and a merchant shopping site, or a group comprising one shopping service, one marketplace, and three merchants.) That is, a channel represents one or more Internet sites where products are promoted and/or sold, and where some commonality makes it worthwhile to study sales of such sites together.
One technique that is available today to assist merchants in optimizing their business is “A/B Testing.” This service, offered by providers such as Optimost and Offermatica, is typically used to evaluate web site layout, product placement, or the effectiveness of different promotional offers. For instance, if a merchant desires to know if its profits will be better by offering customers a discount of $10 on a $100 order or $20 on a $200 order, an A/B test might be appropriate. Traditionally, an A/B test provides one variation (the “A” purchase option) to a statistically significant sample of web site users and another variation (the “B” purchase option) to another statistically significant group of uses. (Note that the number of samples required for a statistically significant test is a function of the amount of performance difference between the “A” variation and the “B” variation, and is not generally known before the test.) The A/B test provider then measures the effectiveness or profitability of each option and determines if “A” or “B” (if either) results in better performance than a baseline option, typically the current offer.
Recently, A/B testing providers have expanded into multi-variate testing where multiple variations on multiple dimensions of a purchase option may be tested simultaneously. For example, a web site might test two different site layouts and two different promotions simultaneously, resulting in four possible combinations being tested. If a merchant attempts to test many different choices of multiple variables simultaneously, this multivariate approach may require an unrealistic number of experiments to determine reasonable solutions. Although A/B and related multivariate testing can be used to optimize product prices, coupon offers, or shipping promotions, one known problem with A/B testing is that it treats variables as discrete choices rather than recognizing that some of these variables may be continuous, non-linear, or even discontinuous. It is reasonable to expect that a price-demand curve will often have these characteristics and may not be most efficiently and accurately found by A/B testing. For more detail on these problems, one can review “AB Testing: Too Little, Too Early?” by John Quarto-vonTivadar, Future Now, Inc., 2005, or U.S. Patent Publication 20040204979). As a further example of A/B testing limitations, consider a product that has a peak in sales at a price of $95, but has identical, lower sales at prices of $90 and $100. If the merchant attempts A/B testing at $90 and $100, the merchant will not see any performance difference and will never find the ideal price of $95 because that price was never evaluated. Another limitation of A/B testing to consider is that it only looks at the merchant's pricing and sales data. A merchant might more quickly identify the peak profit point of the price-demand curve by observing prices at which other merchants are offering the product. For the above reasons, there is a need in the art for a better price optimization technique than A/B testing.
Typically, comparison-shopping services do not process transactions directly (although Amazon and eBay are exceptions). Instead they forward prospective customers to the merchant's website. The comparison services receive revenue either ‘per click’ for directing customers to a merchant website or on a pay-per-click advertising model for ads that are shown alongside the merchant's price data on the comparison site.
Typically, online stores offer a single price per product regardless of from which referring site a customer arrived at the merchant's site. Therefore, if a merchant identifies that a competitor on a particular comparison site is selling a product for a lower price and matches or beats the offer, the merchant will change its price on its website, which is then the offered price for all customers, whether or not they discovered the merchant from the particular comparison shopping service where the merchant was trying to maintain price competitiveness. Thus the merchant may lower its profit margin for all orders of that product.
Further, prior art comparison-shopping services typically compare a single price per product per merchant. Because some sales channels, such as auction sites, support multiple prices such as buy-it-now, bid start and reserve prices, it may be more useful for the merchant to address separately these multiple types of pricing per product for the merchant's various on-line sales channels.
Further, many comparison-shopping services compare the product price but not the “total price” after including shipping charges to a certain destination. So two merchants may charge the same for the product but their shipping prices may differ considerably.
A key problem to solve is that merchants need to use comparison-shopping services to attract customers but need a better way to optimize their profit margin based upon the information available to the customer at the time of their shopping. For example, a merchant that normally sells a product for $200 may be willing to sell it for $150 to match a competitor on a comparison site. But the merchant only wants to offer the $150 price if the customer is at a shopping comparison site where competitors are offering a price such that $150 may be attractive. If other merchants on the comparison-shopping site are offering extremely low prices, the merchant may need also to determine whether its cost structure and overall selling strategy permit it to offer an equivalent or lower price. To do this requires more functionality, optimization logic, and tighter integration with e-commerce stores than is offered by existing e-commerce offerings.
Another problem that merchants face is knowing what prices the market will bear. For instance, a product selling well at a price of $12.50 may sell equally well at $12.75, but then not sell well at $12.99. Traditionally, brick and mortar merchants may estimate price and demand curves via ‘test marketing’ that offers different prices or products to customers of stores in different locales. However, this form of test marketing does not apply to e-commerce stores, because the location of the computers hosting the store has no relevance to the location of the customer.
A further problem is how a merchant should decide which of its products to sell on which sales channels, because the sales channels have different pricing models that include listing fees, insertion fees, final value fee at sale, upload fee, store fee, picture fee, fees based on category, credit card fee and transaction fee. Determining the true cost of a channel is important to merchants, since a product's total channel cost affects the effectiveness of the channel. The price of product is also a consideration; some channels' costs are prohibitively expensive for low-cost-low margin products while others are not effective for high-priced products. Another problem is when to re-list or when to abandon auction sales that did not result in finding a buyer. For example if sales channel “A” charges a flat rate of $2.00 for each product sold, then a merchant selling products with profit margins that are less than $2.00 will lose money using channel “A”. However, if sales channel “B” charges 1% of product cost, then the merchant can sell those same products with a margin of less than $2.00 but greater than 1% of product cost on channel “B” and still make a profit.
It would be desirable to have tools to assist on-line sellers in selecting prices that improve sales and profitability.